Capturing researcher expertise through MeSH classification

For a large research institution and a broad research discipline such as the life sciences, it is a highly important and very challenging task to capture each researcher's expertise, and to match researchers by expertise to assist in identifying inter-disciplinary collaboration opportunities and in making informed policy decisions. The challenges are multi-dimensional, stemming from the needs to (a) provide thorough coverage of the breadth and depth of the disciplinary areas, (b) develop accurate representation of researcher's expertise, and (c) process large volumes of data efficiently. Medical Subject Headings (MeSH), a comprehensive
taxonomy for the life sciences, has been widely used for indexing MEDLINE publications. In this paper, we present a novel framework for capturing and matching research expertise based on knowledge encoded in MeSH. Specifically, (1) we design a novel and effective hybrid MeSH classification algorithm by combining state-of-the-art methods, and (2) using MeSH terms aggregated from a researcher's publications, we design a
researcher matching algorithm based on semantic similarity that takes into consideration the structure of the MeSH taxonomy.